Flip-avoiding interpolating surface registration for skull reconstruction
XIE SHUDONG LEOW WEE KHENG LIM THIAM CHYE Lee Hanjing
Lee Hanjing
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Abstract
Skull reconstruction is an important and challenging task in craniofacial surgery planning, forensic investigation and anthropological studies. Existing methods typically reconstruct approximating surfaces that regard corresponding points on the target skull as soft constraints, thus incurring non-zero error even for non-defective parts and high overall reconstruction error. This paper proposes a novel geometric reconstruction method that non-rigidly registers an interpolating reference surface that regards corresponding target points as hard constraints, thus achieving low reconstruction error. To overcome the shortcoming of interpolating a surface, a flip-avoiding method is used to detect and exclude conflicting hard constraints that would otherwise cause surface patches to flip and self-intersect. Comprehensive test results show that our method is more accurate and robust than existing skull reconstruction methods. By incorporating symmetry constraints, it can produce more symmetric and normal results than other methods in reconstructing defective skulls with a large number of defects. It is robust against severe outliers such as radiation artifacts in computed tomography due to dental implants. In addition, test results also show that our method outperforms thin-plate spline for model resampling, which enables the active shape model to yield more accurate reconstruction results. As the reconstruction accuracy of defective parts varies with the use of different reference models, we also study the implication of reference model selection for skull reconstruction.
Keywords
Laplacian deformation, interpolating surface, model resampling, non-rigid registration, skull reconstruction
Source Title
International Journal of Medical Robotics and Computer Assisted Surgery
Publisher
John Wiley and Sons Ltd
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Date
2018
DOI
10.1002/rcs.1906
Type
Article